Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI
作者：
Highlights：
• We propose a framework for defining different types of explanations of AI systems.
• We contextualize current XAI discourses within the proposed framework.
• We highlight two broad perspectives for defining quality criteria for explainability.
• We discuss the relevance of our framework in light of current and upcoming AI regulation.
• We confer fundamental aspects for future research of XAI scholars.
摘要
•We propose a framework for defining different types of explanations of AI systems.•We contextualize current XAI discourses within the proposed framework.•We highlight two broad perspectives for defining quality criteria for explainability.•We discuss the relevance of our framework in light of current and upcoming AI regulation.•We confer fundamental aspects for future research of XAI scholars.
论文关键词：Explainable AI,XAI,Explanations,Taxonomy,Artificial intelligence,Machine learning
论文评审过程：Received 31 March 2022, Revised 20 July 2022, Accepted 20 September 2022, Available online 24 September 2022, Version of Record 6 October 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118888
